Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery

The wide field of view (WFV) imaging system onboard the Chinese GaoFen-1 (GF-1) optical satellite has a 16-m resolution and four-day revisit cycle for large-scale Earth observation. The advantages of the high temporal-spatial resolution and the wide field of view make the GF-1 WFV imagery very popular. However, cloud cover is an inevitable problem in GF-1 WFV imagery, which influences its precise application. Accurate cloud and cloud shadow detection in GF-1 WFV imagery is quite difficult due to the fact that there are only three visible bands and one near-infrared band. In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery. The MFC algorithm first implements threshold segmentation based on the spectral features and mask refinement based on guided filtering to generate a preliminary cloud mask. The geometric features are then used in combination with the texture features to improve the cloud detection results and produce the final cloud mask. Finally, the cloud shadow mask can be acquired by means of the cloud and shadow matching and follow-up correction process. The method was validated using 108 globally distributed scenes. The results indicate that MFC performs well under most conditions, and the average overall accuracy of MFC cloud detection is as high as 96.8%. In the contrastive analysis with the official provided cloud fractions, MFC shows a significant improvement in cloud fraction estimation, and achieves a high accuracy for the cloud and cloud shadow detection in the GF-1 WFV imagery with fewer spectral bands. The proposed method could be used as a preprocessing step in the future to monitor land-cover change, and it could also be easily extended to other optical satellite imagery which has a similar spectral setting.Comment: This manuscript has been accepted for publication in Remote Sensing of Environment, vol. 191, pp.342-358, 2017. (http://www.sciencedirect.com/science/article/pii/S003442571730038X

The Tianlai Cylinder Pathfinder array: System functions and basic performance analysis

The Tianlai Cylinder Pathfinder is a radio interferometer array designed to test techniques for 21 cm intensity mapping in the post-reionization Universe, with the ultimate aim of mapping the large scale structure and measuring cosmological parameters such as the dark energy equation of state. Each of its three parallel cylinder reflectors is oriented in the north-south direction, and the array has a large field of view. As the Earth rotates, the northern sky is observed by drift scanning. The array is located in Hongliuxia, a radio-quiet site in Xinjiang, and saw its first light in September 2016. In this first data analysis paper for the Tianlai cylinder array, we discuss the sub-system qualification tests, and present basic system performance obtained from preliminary analysis of the commissioning observations during 2016-2018. We show typical interferometric visibility data, from which we derive the actual beam profile in the east-west direction and the frequency band-pass response. We describe also the calibration process to determine the complex gains for the array elements, either using bright astronomical point sources, or an artificial on site calibrator source, and discuss the instrument response stability, crucial for transit interferometry. Based on this analysis, we find a system temperature of about 90 K, and we also estimate the sensitivity of the array

Human-Level Control through Directly-Trained Deep Spiking Q-Networks

As the third-generation neural networks, Spiking Neural Networks (SNNs) have great potential on neuromorphic hardware because of their high energy-efficiency. However, Deep Spiking Reinforcement Learning (DSRL), i.e., the Reinforcement Learning (RL) based on SNNs, is still in its preliminary stage due to the binary output and the non-differentiable property of the spiking function. To address these issues, we propose a Deep Spiking Q-Network (DSQN) in this paper. Specifically, we propose a directly-trained deep spiking reinforcement learning architecture based on the Leaky Integrate-and-Fire (LIF) neurons and Deep Q-Network (DQN). Then, we adapt a direct spiking learning algorithm for the Deep Spiking Q-Network. We further demonstrate the advantages of using LIF neurons in DSQN theoretically. Comprehensive experiments have been conducted on 17 top-performing Atari games to compare our method with the state-of-the-art conversion method. The experimental results demonstrate the superiority of our method in terms of performance, stability, robustness and energy-efficiency. To the best of our knowledge, our work is the first one to achieve state-of-the-art performance on multiple Atari games with the directly-trained SNN.Comment: Accepted by IEEE Transactions on Cybernetic

Plasma Catestatin: A Useful Biomarker for Coronary Collateral Development with Chronic Myocardial Ischemia.

BACKGROUNDS:Catestatin is an endogenous multifunctional neuroendocrinepeptide. Recently, catestatin was discovered as a novel angiogenic cytokine. The study was to investigate the associations between endogenous catestatin and coronary collateral development among the patients with chronic myocardial ischemia. METHODS:Thirty-eight patients with coronary artery chronic total occlusions (CTO) (CTO group) and 38 patients with normal coronary arteries (normal group) were enrolled in the series. Among the patients with CTO, coronary collateral development was graded according to the Rentrop score method. Rentrop score 0-1 collateral development was regarded as poor collateral group and 2-3 collateral development was regarded as good collateral group. Plasma catestatin level and vascular endothelial growth factor (VEGF) were measured by ELISA kits. RESULTS:The plasma catestatin levels in CTO group were significantly higher than that in normal group (1.97±1.01 vs 1.36±0.97ng/ml, p = 0.009). In the CTO group, the patients with good collateral development had significantly higher catestatin and VEGF levels than those with poor collateral development (2.36±0.73 vs 1.61±1.12 ng/ml, p = 0.018; 425.23±140.10 vs 238.48±101.00pg/mL, p<0.001). There is a positive correlation between plasma catestatin levels and Rentrop scores (r = 0.40, p = 0.013) among the patients with CTO. However, there is no correlations between plasma catestatin levels and VEGF (r = -0.06, p = 0.744). In the multiple linear regression models, plasma catestatin level was one of the independent factors of coronary collateral development after adjustment for confounders. CONCLUSIONS:Plasma catestatin was associated with coronary collateral developments. It may be a useful biomarker for coronary collateral development and potential target for therapeutic angiogenesis in patients with CTO

Efficient Group Collaboration for Sensing Time Redundancy Optimization in Mobile Crowd Sensing

In mobile crowd sensing (MCS), complex tasks often require collaboration among multiple workers with diverse expertise and sensors. However, few studies consider the sensing time redundancy of multiple workers to complete a task collaboratively, and the subjective and objective collaboration willingness of participating workers in forming collaboration groups for different tasks. If solely focusing on enhancing workers&#x2019; willingness to collaborate, it cannot guarantee the minimum time redundancy within the collaboration group, resulting in a decrease in the group&#x2019;s efficiency. Similarly, if only aiming to reduce sensing time redundancy among the workers in the collaboration group, it may lead to a loss of workers&#x2019; willingness to collaborate, and the diminished motivation among workers will consequently reduce the group&#x2019;s efficiency. To address these challenges, this paper proposes EGC-STRO, a method for forming efficient collaboration groups in MCS that optimizes sensing time redundancy while balancing the workers&#x2019; cooperation willingness as constraints. First, this method proposes an evaluation indicator to select workers who meet their reward expectations, i.e., objective collaboration willingness, and uses an incentive mechanism based on bargaining game to maximize the overall interests. Furthermore, subjective collaboration willingness is defined and a collaboration worker selection algorithm is designed. The algorithm adds workers who meet both subjective and objective willingness requirements to the candidate set and selects workers with the smallest sensing redundancy time in the worker candidate set to join the final collaboration group. Simulation results demonstrate that compared with the baseline methods, our proposed EGC-STRO increases the worker engagement by about 5%-20%, increases the task coverage by 6%-25%, increases the platform utility by 17%-50%, and increases the worker utility by 20%-60%

Efficient Group Collaboration for Sensing Time Redundancy Optimization in Mobile Crowd Sensing

In mobile crowd sensing (MCS), complex tasks often require collaboration among multiple workers with diverse expertise and sensors. However, few studies consider the sensing time redundancy of multiple workers to complete a task collaboratively, and the subjective and objective collaboration willingness of participating workers in forming collaboration groups for different tasks. If solely focusing on enhancing workers&amp;#x2019; willingness to collaborate, it cannot guarantee the minimum time redundancy within the collaboration group, resulting in a decrease in the group&amp;#x2019;s efficiency. Similarly, if only aiming to reduce sensing time redundancy among the workers in the collaboration group, it may lead to a loss of workers&amp;#x2019; willingness to collaborate, and the diminished motivation among workers will consequently reduce the group&amp;#x2019;s efficiency. To address these challenges, this paper proposes EGC-STRO, a method for forming efficient collaboration groups in MCS that optimizes sensing time redundancy while balancing the workers&amp;#x2019; cooperation willingness as constraints. First, this method proposes an evaluation indicator to select workers who meet their reward expectations, i.e., objective collaboration willingness, and uses an incentive mechanism based on bargaining game to maximize the overall interests. Furthermore, subjective collaboration willingness is defined and a collaboration worker selection algorithm is designed. The algorithm adds workers who meet both subjective and objective willingness requirements to the candidate set and selects workers with the smallest sensing redundancy time in the worker candidate set to join the final collaboration group. Simulation results demonstrate that compared with the baseline methods, our proposed EGC-STRO increases the worker engagement by about 5%-20%, increases the task coverage by 6%-25%, increases the platform utility by 17%-50%, and increases the worker utility by 20%-60%.</p

Strength–Plasticity Relationship and Intragranular Nanophase Distribution of Hybrid (GNS + SiCnp)/Al Composites Based on Heat Treatment

The distribution of reinforcements and interfacial bonding state with the metal matrix are crucial factors in achieving excellent comprehensive mechanical properties for aluminum (Al) matrix composites. Normally, after heat treatment, graphene nanosheets (GNSs)/Al composites experience a significant loss of strength. Here, better performance of GNS/Al was explored with a hybrid strategy by introducing 0.9 vol.% silicon carbide nanoparticles (SiCnp) into the composite. Pre-ball milling of Al powders and 0.9 vol.% SiCnp gained Al flakes that provided a large dispersion area for 3.0 vol.% GNS during the shift speed ball milling process, leading to uniformly dispersed GNS for both as-sintered and as-extruded (0.9 vol.% SiCnp + 3.0 vol.% GNS)/Al. High-temperature heat treatment at 600 °C for 60 min was performed on the as-extruded composite, giving rise to intragranular distribution of SiCnp due to recrystallization and grain growth of the Al matrix. Meanwhile, nanoscale Al4C3, which can act as an additional reinforcing nanoparticle, was generated because of an appropriate interfacial reaction between GNS and Al. The intragranular distribution of both nanoparticles improves the Al matrix continuity of composites and plays a key role in ensuring the plasticity of composites. As a result, the work hardening ability of the heat-treated hybrid (0.9 vol.% SiCnp + 3.0 vol.% GNS)/Al composite was well improved, and the tensile elongation increased by 42.7% with little loss of the strength. The present work provides a new strategy in achieving coordination on strength–plasticity of Al matrix composites

Direct ink writing of fluoropolymer/CNT-based superhydrophobic and corrosion-resistant electrodes for droplet energy harvesters and self-powered electronic skins

Self-powered devices and systems that operate by harnessing environmental mechanical energies including raindrops and body motions have been extensively explored owing to their promising applications. In practical applications, these devices are often exposed to humid conditions or directly contact aqueous solutions. Here, we report the development of chemically inert and superhydrophobic electrode based on fluoropolymer (FP)/carbon-nanotube (CNT) that circumvents undesired metal electrode corrosion, deformation, and damage in harsh environments. The electrode surface can be patterned on flexible surfaces via direct ink writing (DIW), and no damage or corrosion is detected even being bent 10,000 times or immersed into salt/acid/alkaline solutions for 20 h. The integration of such robust electrodes with hydrophobic tribo-materials enables the construction of droplet-based electricity generators (DEGs) that exhibit an instantaneous current and power outputs of 2 mA and 0.12 W, respectively, and that light up 50 LEDs by one water droplet. Self-powered touch sensing function is also demonstrated on FP/CNT-based electronic skin, offering the broad applicability of the proposed electrode preparation strategy for self-powered devices

Effect of Al Addition on Grain Refinement and Phase Transformation of the Mg-Gd-Y-Zn-Mn Alloy Containing LPSO Phase

The effect of 0&ndash;1.0 at.% Al additions on grain refinement and phase transformation of the Mg-2.0Gd-1.2Y-0.5Zn-0.2Mn (at.%) alloy containing a long period stacking ordered (LPSO) phase was investigated in this work. The addition of Al promoted the formation of the Al2RE phase in the Mg-2.0Gd-1.2Y-0.5Zn-0.2Mn (at.%) alloy, and the dominant secondary phases in the as-cast Mg-2.0Gd-1.2Y-0.5Zn-0.2Mn-1.0Al (at.%) alloy were the Mg3RE phase, LPSO phase, and Al2RE phase. With increased Al addition, the area fraction of the Al2RE phase increased monotonously, while the area fraction of LPSO phase and Mg3RE phase decreased gradually. The orientation relationship between the Al2RE phase and the &alpha;-Mg matrix was determined to be &lt;112&gt;Al2RE//&lt;112&macr;0&gt;&alpha;-Mg, {101}Al2RE//{101&macr;0}&alpha;-Mg, which was not affected by Zn and Mn concentrations in the Al2RE phase. Since the Al2RE particles with a size more than 6 &mu;m located at the center of grains could act as nucleants for &alpha;-Mg grains, the average grain size of the as-cast alloys decreased from 276 &mu;m to 49 &mu;m after 1.0% Al addition. The effect of the Al addition on the grain refinement of the Mg-2.0Gd-1.2Y-0.5Zn-0.2Mn alloy was comparable to that of the Zr refined counterpart